|
Description
|
RepLab is a competitive evaluation exercise for Online Reputation Management systems organized as an activity of CLEF. RepLab 2013 focused on the task of monitoring the reputation of entities (companies, organizations, celebrities, etc.) on Twitter. The monitoring task for analysts consists of searching the stream of tweets for potential mentions to the entity, filtering those that do refer to the entity, detecting topics (i.e., clustering tweets by subject) and ranking them based on the degree to which they signal reputation alerts (i.e., issues that may have a substantial impact on the reputation of the entity). The RepLab 2013 task is defined, accordingly, as (multilingual) topic detection combined with priority ranking of the topics, as input for reputation monitoring experts. The detection of reputational polarity (does the tweet have negative/positive implications for the reputation of the entity?) is an essential step to assign priority, and was evaluated as a standalone subtask
Application of Formal Concept Analysis (FCA), an exploratory technique for data analysis and organization. In particular, we propose an extension of FCA-based methods for topic detection applied in the literature by applying the stability concept for the topic selection. The hypothesis is that FCA will enable the better organization of the data and stability the better selection of topics based on this data organization, thus better fulfilling the task requirements by improving the quality and accuracy of the topic detection process
FCA.tar.gz (about 3MB) This file contains the FCA implementation as well as the input files for the execution The dataset can be downloaded from the official RepLab webpage: http://nlp.uned.es/replab2013/.
|
